Day-Ahead Optimal Joint Scheduling Model of Electric and Natural Gas Appliances for Home Integrated Energy Management

Li, Kangping; Zhang, Peng; Li, Gang; Wang, Fei; Mi, Zengqiang; Chen, Hongyu

doi:10.1109/access.2019.2941238

Cited by 17 publications

(6 citation statements)

References 37 publications

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“…The HEMS-based models are also suitable for the simulation of certain appliances and multiple appliances simultaneously [8,38,60]. In addition, smart control with the comfort aspects has been applied to some representative appliances in [63] and to a dedicated appliance like AC in [64,68].…”

Section: Analysis and Discussionmentioning

confidence: 99%

“…Moreover, the results obtained in the final list of studies show that efficiency is being achieved in most studies that accord with the aims of most of these research studies. Most results have shown that the efficiency share is being achieved in the installed capacity reduction [1,2,62] and the cost [8,18,61,63,67,70], including emissions reductions [69] and peak consumption reductions [64][65][66]68]. However, the centralized AC in [64] increased the total energy consumption by 13.3%.…”

Section: Analysis and Discussionmentioning

confidence: 99%

“…According to Wang et al (2018), quoted in [59], the HEMS allows for "optimal scheduling of each electrical appliance and distributes generators according to an objective function (e.g., the energy cost) predefined by the customer or an aggregator and according to some external information (e.g., weather, electricity price, incentive signal, etc.) in order to help residential customers to efficiently manage their energy consumption" [8]. Each method, such as the fuzzy logic model (a16), clustering technique (a3), DSM-based model (a15), and stochastic thermal model (a10), was proposed by a single study, which applies to a single appliance.…”

Section: Extracting Datamentioning

confidence: 99%

“…Most of the results from the final list of studies have been validated in comparison with other studies, techniques, or scenarios [1,2,8,20,61,67]. Some studies have been tested on more than one case or model [18,62,64,70], while others have been compared with the real data [68,69].…”

Section: Dsm-based Model Ac -mentioning

confidence: 99%

“…One load-management strategy in DSM is load-shifting (Figure 1), a technique to shift peakhour demand to off-peak hours by reallocating the load demand [6] without changing the total energy consumption [7]. This can also reduce the cost [1,6,[8][9][10][11][12][13][14][15] as well as the capacity of the electricity grid. In practice, Yilmaz et al (2019), quoted in [16], defined load-shifting as a "process where consumers time-shift demand, either through behaviour change or automation, in response to particular conditions within the electricity system, and [it] is therefore a potential solution to equilibrate the network".…”

Section: Introductionmentioning

confidence: 99%

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A Systematicity Review on Residential Electricity Load-Shifting at the Appliance Level

Manembu,

Kewo,

Bramstoft

et al. 2023

Energies

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Load-shifting is a demand-side management (DSM) strategy to support the efficiency of the electricity grid during hours of peak demand. Load-shifting at the appliance level is an interesting topic to review, since appliance usage is one of the main inputs of the load-profile analysis. More literature reviews on load-shifting at the appliance level are required, as this is a specific issue in the body of literature on load-profile research, though only a limited number of studies are available at this time. It is also essential to focus on appliance usage patterns to improve our understanding of the impacts and characteristics of different appliances. Existing studies on load-shifting have used commonly structured literature reviews; our work addresses the transparency of each stage and substage in the selection of the final list of studies. The findings show that efficiency has been achieved in installed-capacity reductions; costs, including those of emission reductions; and peak consumption reductions. The most frequently used method in load-shifting at the appliance level is to develop load-shifting optimization algorithms. This work contributes by providing a transparent process of drawing up a systematicity literature review as a source of knowledge and grounded theory. It also contributes to specific research on load-shifting at the appliance level by highlighting and discussing the key findings for the reader. In particular, it contributes to improving energy efficiency by describing load-shifting methods at the appliance level and identifying both controllable and uncontrollable appliances. This detailed literature review at the appliance level can make valuable contributions in support of decision- and policymaking by illuminating new dynamic systems specifically in load-shifting and in demand-side management in general for energy efficiency purposes.

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